Lead AI Solutions Delivery Engineer

DocuSign DocuSign · Enterprise · Washington, WA · Sales & Partnerships

Lead AI Solutions Delivery Engineer at DocuSign, focused on deploying GenAI and LLMs for enterprise customers. This role involves designing and building AI-enabled workflows, integrating Docusign's AI capabilities with third-party AI ecosystems (OpenAI, Microsoft CoPilot, Claude), and optimizing agreement lifecycles. The engineer will also develop data pipelines, microservices, and system architectures, and contribute to the product roadmap based on field learnings. Requires strong experience in customer-facing technical delivery and implementing LLMs/GenAI applications.

What you'd actually do

  1. Design and lead cross functional customer-facing engagements to develop and deploy a modern Agentic vision of Agreement Management business processes
  2. Design, build, and deploy AI enabled workflows and solutions that integrate Docusign AI capabilities including but not limited to, Docusign’s MCP Server and APIs, Docusign Web Forms backed by APIs to third party systems, Custom Extractions, Docusign skills in third party agentic platforms (e.g. Docusign’s integration with Open AI, Claude Cowork, Microsoft CoPilot, Harvey and Legora) and other similar customer Agentic Tech stacks to automate and optimize customer business processes
  3. Execute deployment 'firsts' to prove out, capture, and productionize new Agentic implementation patterns, repeatable use cases and toolkits
  4. Run technical demos, trainings, and workshops for technical and non-technical audiences
  5. Partner directly with customer stakeholders, Docusign product, engineering, success, services and partner team members and third party system integrators to translate open-ended operational business requirements into production level AI enabled business solutions

Skills

Required

  • Python, Java, C++, or systems fundamentals
  • 1.5+ years of experience implementing LLMs, production-grade GenAI applications, autonomous agents, or orchestration frameworks for production level software
  • Experience working directly with customers during POCs, architecture reviews, and technical evaluations

Nice to have

  • Prior experience as a forward deployed engineer, customer-facing technical lead, startup CTO, enterprise architect or software engineer with consulting experience
  • Experience deploying autonomous agents or AI orchestration frameworks within highly regulated environments such as finance or healthcare
  • Deep understanding of data security, compliance frameworks, and isolated enterprise cloud deployments
  • Ability to collaborate cross functionally and “love of learning” posture required to support the rapid development in the AI space
  • Experience designing agent-based or LLM-powered applications beyond simulation

What the JD emphasized

  • proven track record deploying GenAI and LLMs in production
  • Experience implementing LLMs, production-grade GenAI applications, autonomous agents, or orchestration frameworks for production level software
  • Experience deploying autonomous agents or AI orchestration frameworks within highly regulated environments such as finance or healthcare

Other signals

  • customer-facing
  • deploying GenAI and LLMs in production
  • designing bespoke architectures
  • engineering prompts
  • autonomous AI tools